962 results about "Data optimization" patented technology

The invention discloses a cloud computing resource optimization method based on intelligent scheduling, and belongs to the technical field of cloud computing resource processing. The method comprises the steps of obtaining real-time operation data of target data in a data optimization detection range, collecting historical resource scheduling records and task execution logs, and constructing a multi-dimensional resource state data set; according to the method, multi-objective optimization, simulation verification and reinforcement learning feedback in the step S5 are carried out, a perception-prediction-scheduling-monitoring-optimization closed-loop mechanism is constructed, the resource utilization rate, the response time and the energy consumption cost of a multi-objective optimization function are balanced, and a particle swarm optimization algorithm is combined with simulation verification to generate a global optimal strategy; and reinforcement learning dynamically adjusts model parameters by taking the execution deviation as a reward signal, continuously updates a resource perception dimension and a prediction model, realizes continuous iterative upgrade of a resource optimization effect, and performs optimization processing on cloud computing resource optimization based on intelligent scheduling.

The invention relates to the technical field of network security, in particular to a dynamic access blocking method based on zero trust. Comprising the following steps: step 1, collecting whole network flow data in real time in a bypass monitoring mode through a flow mirroring function of a network switch, performing deep packet inspection analysis on the collected original flow data, and extracting network flow characteristic parameters; 2, maintaining a dynamic identity information base; 3, performing real-time behavior analysis on each network session; 4, according to the risk assessment result and the real-time security context, generating a dynamic access control strategy based on a minimum permission principle; 5, implementing access control at the network execution point; and 6, continuously monitoring the network flow and the strategy execution effect, collecting feedback data, optimizing the risk assessment model and the strategy generation algorithm based on the feedback data, and forming closed-loop control. By dynamically updating the identity and asset information, the system can identify new assets or changes in real time, so that the adaptability and response capability of a network environment are improved.

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention provides a data optimization processing method and system for edge calculation in architectural design. According to the method, geometric deviation and attribute change characteristics between building design models are obtained, and a version conflict identification graph for marking high-conflict areas is generated through topological relation comparison; deploying an incremental synchronous controller at an edge computing node of a physical layer, dynamically intercepting a high-conflict area association difference data fragment and compressing the high-conflict area association difference data fragment into an incremental data packet; analyzing a dependency topological relation between the models to predict a conflict propagation path; converting the path into a combined operation instruction conforming to a design rule through a collaborative modeling engine; and executing the instruction to integrate the difference, and outputting a conflict-free multi-building design integrated model after the models are fused. According to the method, second-level dynamic resolution and automatic fusion of building model conflicts are realized, the design iteration efficiency is improved, and the construction rework risk is eliminated.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention discloses a personalized rehabilitation training method based on state monitoring, and particularly relates to the technical field of intelligent medical treatment. According to the method, the technical problems of insufficient individuation, poor safety, limited effect and the like caused by single monitoring dimension, lagging adjustment strategy and lack of self-optimization capability in the existing rehabilitation training are solved. According to the scheme, physiological, motion and subjective multi-dimensional data of a patient are synchronously collected through a multi-source sensor and input into a machine learning model for real-time state evaluation after fusion processing, a personalized training scheme is generated and executed accordingly, meanwhile, parameters are dynamically adjusted according to real-time monitoring data in training, and the training efficiency is improved. And the model and the strategy are optimized by analyzing historical data after the period is ended. According to the invention, safe, accurate and self-adaptive evolution personalized rehabilitation training is realized, and the safety, adaptability and rehabilitation effect of training are improved.

The invention belongs to the technical field of spacecraft pose estimation, and particularly relates to a cross-domain spacecraft pose estimation method based on mask self-distillation domain adaptation, and the method comprises the steps: 1, inputting a complete image, and employing a Faster R-CNN algorithm to position a spacecraft bounding box; the robustness of the model is improved by applying a track environment data enhancement strategy; and extracting a target ROI region as key point regression network input based on the detection frame. 2, dividing ROI (Region of Interest) data of a source domain and a target domain; and optimizing heat map supervision loss learning key point positioning knowledge. 3, inputting random mask enhanced target domain data into the student model; inputting original target domain data into the teacher model; a learnable shared prototype space is constructed, and self-distillation is guided through heat map consistency loss and semantic consistency loss. And 4, jointly optimizing the loss of the key point regression network 3, and realizing progressive migration of source domain annotation knowledge to a target domain. And 5, solving the 6D pose of the spacecraft relative to the camera through the EPnP. According to the invention, robust six-degree-of-freedom pose estimation of the target spacecraft is realized.

The invention provides a syntactic structure tree-based code change influence intelligent analysis method and system, and the method comprises the steps: extracting a control flow, a data flow and a call flow according to an AST generated through the analysis of an original code and a changed code, and constructing a static dependency graph SDG containing a multi-dimensional dependency relationship; the method comprises the steps that a base layer is used for comparing AST differences before and after code change and generating grammatical structure change feature vectors; the service layer matches a corresponding service label for the changed AST node by using the service domain template; the change labeling layer constructs a cross-version dependency chain for the change AST nodes and generates historical feature vectors; and calculating a risk value of changing the AST node by utilizing a feature calculation result and combining with a multi-dimensional influence diffusion algorithm, and automatically generating a decision suggestion. According to the method, multiple dimensions of a control flow, a data flow and a call flow are deepened, risk prediction is optimized by utilizing natural language processing and historical change data, and the influence brought by code change is assisted to be better understood and managed.

The invention relates to the technical field of industrial unmanned aerial vehicle application and intelligent scheduling, in particular to an unmanned aerial vehicle road inspection task intelligent planning management system which comprises a task input module, an intelligent planning engine, a task execution management module, a data center and a feedback optimization module. The task input module receives, verifies and analyzes inspection task requirements and road basic information; the intelligent planning engine decomposes task priorities based on highway multi-dimensional attributes, dynamically allocates unmanned aerial vehicle resources and generates an optimal path meeting multiple constraints; the task execution management module issues an adaptation instruction, monitors the state of the unmanned aerial vehicle and processes inspection data; the data center uniformly stores whole-process data; the feedback optimization module optimizes the decision model based on historical data. The method improves the inspection planning precision and the resource utilization rate, guarantees the data quality, achieves the self-evolution, and guarantees the high efficiency and reliability of the road inspection.

The invention provides a virtual fitting recommendation method based on an artificial intelligence technology, and the method comprises the steps: firstly, obtaining the body type data of a user through a three-dimensional scanning device, marking key points through a deep learning network, and inputting body type features and clothing preference labels; thirdly, in combination with the dynamic fitting relation between the clothes and the human body, virtual fitting verification nodes are set, and spatial topology optimization is carried out to generate a virtual fitting mapping layout; then, user data and mapping layout are called from the clothing simulation layer, dynamic deformation simulation is carried out based on the action posture sequence, visual fitting data are extracted, the fitting matching degree is calculated, and a fitting degree evaluation matrix is formed; and then, the interaction feedback layer collects real-time interaction behavior data of the user, performs action calibration, and generates a recommendation correction instruction through comparative analysis. And finally, an optimization decision-making layer optimizes a recommendation strategy and deformation parameters based on historical data, and generates a personalized try-on quality report. According to the invention, the fidelity of virtual fitting, the user experience and the recommendation accuracy can be improved.

The invention, which relates to the technical field of energy management, discloses a multi-target collaborative optimization prediction system and method for regional-level water-wind-light power generation power, and the system comprises a data collection module which is used for obtaining power generation power data, meteorological environment data and power grid load demand data in real time; the optimization processor is used for receiving the data fed back by the data acquisition module, and constructing a multi-target optimization model based on spatial-temporal feature extraction to generate a plurality of candidate optimization paths; the screening module is used for screening the candidate paths through a dynamic weight distribution algorithm and screening out an optimal collaborative optimization path; and the dynamic feedback module is used for carrying out online correction on the optimal collaborative optimization path in the real-time optimization process according to the power grid frequency deviation, the energy storage charge state and the meteorological abrupt change signal. The optimization processor constructs the multi-target optimization model based on the spatial-temporal characteristics, generates a plurality of candidate optimization paths and screens out the optimal path, and compared with a traditional single energy prediction and simple scheduling mode, the precision of power generation power prediction is improved.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

The invention relates to the technical field of vibration noise optimization, in particular to a vibration noise optimization method and system of an electric drive assembly. The method comprises the following steps of collecting multi-working-condition vibration noise of a transmission motor in an electric drive assembly and performing frequency domain conversion to generate vibration noise frequency domain data; on the basis of the data, carrying out gear meshing stress unbalance loading limit calculation, carrying out tooth surface pitting corrosion cycle ductility simulation, and identifying a bearing clearance expansion increment; optimizing the bearing structure according to the bearing clearance expansion incremental data to obtain bearing structure optimization data; performing tooth direction drum shape modification analysis according to the tooth surface pitting corrosion data to obtain modification data; and finally, carrying out anti-vibration strengthening design by combining bearing optimization and tooth direction modification data, generating structural anti-vibration strengthening design data, and sending the structural anti-vibration strengthening design data to a terminal so as to execute structural optimization and reduce the influence of vibration noise. According to the method, the vibration noise optimization technology is optimized, so that the vibration noise optimization technology is more perfect.

The invention discloses a digital human interaction method and system based on a large model. The method comprises the following steps: acquiring a multi-mode instruction; generating contextual information based on the basic settings and historical information of the digital human; inputting the multi-mode instruction and the context information into a core model to obtain an initial text response; adjusting the initial text response based on the emotional state and character traits of the digital person to obtain a text reply; generating an expression reply according to the text reply, and controlling digital human output; receiving feedback information input by a user, and generating interaction data; and optimizing model parameters of the core model based on the interaction data. According to the method and the device, the reply which better fits the user demand and the emotional state is generated by acquiring the multi-mode instruction and combining the basic setting and the historical information of the digital human. The digital human continuously accumulates knowledge from the interaction of the user, and the behavior mode and evolution character are optimized, so that the continuous learning and growth of the digital human are realized.

The invention discloses an integrated circuit equipment data optimization monitoring system and method based on big data, and relates to the technical field of integrated circuit manufacturing. The method is used for solving the problems of insufficient multi-physical field monitoring, difficulty in abnormal traceability and lack of closed-loop control in plasma etching. A plasma sheath thickness inversion model and an etching selection ratio model are constructed by collecting radio frequency reflection phase, mass spectrum ion strength and wafer temperature data, and process parameter-plasma state dynamic coupling is established. Interference image distortion features and electron microscope size data are fused, micro-groove geometric parameters are analyzed, morphology instability risk indexes are generated, and nanoscale early warning is achieved. And designing a dual-channel fusion network, embedding a physical constraint attention mechanism, and generating an etching rate optimization instruction. On the basis of incremental learning, model parameters are updated online, a'monitoring-decision-feedback 'closed-loop system is formed, anomaly detection sensitivity and decision reliability are improved, and technical support is provided for intelligence of integrated circuit equipment.

The invention discloses an operation analysis method and system for a mobile robot fused with an industrial mechanical arm, and the method comprises the steps: receiving a work order, then splitting a task, determining sub-task parameters, and optimizing the predicted time based on historical data; mapping and positioning are synchronized in the moving process, and the positioning precision is guaranteed through an SLAM model and prediction-correction logic; planning a global path and a residence position, and coping with a dynamic obstacle through a weighted scoring method and an MPC model; the operation position is precisely aligned to compensate the ground unevenness error; recognizing a workpiece and selecting an optimal grabbing point; the contact force and rigidity are dynamically adjusted when operation is executed, and automatic retreating and fine adjustment are performed when abnormity occurs; the grabbing quality and the advancing safety are confirmed; and data is updated after ending, and low-power automatic charging is realized. According to the method, the problems that a traditional technology path and operation planning are separated, sensing is single, robustness is poor, an operation strategy is difficult to adapt, and a standardized quick change and calibration mechanism is lacked are solved.

The invention relates to the technical field of drainage pipe network defect detection and computer vision, in particular to a drainage pipe network defect detection method based on data optimization and multi-model fusion, and the method comprises the steps: collecting a video in a pipeline through a detection robot, and carrying out the frame extraction to obtain an original image; carrying out the quality optimization of the image through employing a lens fouling restoration algorithm, an image defogging algorithm and an illumination enhancement algorithm; positioning a defect area by using the target detection model, and performing pixel-level segmentation through the U-Net semantic segmentation model to obtain a defect contour; visual features and morphological features are extracted, feature fusion is carried out in combination with an attention mechanism, and finally a classifier is input to realize accurate identification of defect types. Targeted solutions are provided for multiple common imaging defects in the drainage pipe network, the method can adapt to complex and changeable actual detection environments, good generalization ability and practical value are achieved, and reliable technical support can be provided for intelligent operation and maintenance of the urban drainage pipe network.

Techniques for optimized LLM inference from structured data via intermediate documents (“LLMs”) are disclosed. In an example method, a computing device accesses a database including a set of collections. The computing device generates one or more documents based on a first collection of the set of collections. The computing device determines one or more portions of the one or more documents based on at least one topic of the one or more documents. The computing device generates an embedded representation of each portion. The computing device receives, from a client device, a first query, including at least a first topic. The computing device determines a first portion of the one or more portions based on the first topic. The computing device generates a response based on the first query and the first portion and outputs the response to the client device.

The invention relates to the technical field of computers, in particular to a data sharing management system and method for a database, and the system comprises an identity verification module, a permission adaptation and grading approval module, a risk control module and an iteration module, and the method correspondingly executes the functions of the modules. The identity verification guarantees the legality of an access subject through format verification and multi-dimensional hierarchical authentication, and the intensity of the hierarchical authentication is dynamically adjusted along with the data sensitivity; authority management and control execute differentiation logic according to public, internal and confidential three levels, and fine authorization is achieved; the risk control module monitors abnormal operation in real time and performs grading processing; all operations are recorded in a block chain audit log, and the system realizes dynamic iteration through regular verification and historical data optimization. The data sharing security and cooperation efficiency are considered, the risk controllability and the system adaptability are improved, and the multi-scene data sharing requirements such as enterprise cross-department and external cooperation are met.

The invention discloses a multi-dimensional fault diagnosis system based on cooperation of a transformer substation auxiliary control device and a cloud platform, and the system comprises the steps: collecting multi-modal sensor data through the transformer substation auxiliary control device, carrying out the unified format conversion and normalization processing, and extracting the feature parameters of each modal through a feature extraction algorithm; the model construction module is used for training the historical multi-modal sensor data marked with the fault type label through a cloud platform by utilizing a deep learning algorithm to obtain an initial fault diagnosis model, and optimizing the initial fault diagnosis model according to the preprocessed multi-modal sensor data; a fault diagnosis model of collaborative optimization of the cloud platform and the substation auxiliary control device is obtained; and the fault diagnosis module analyzes each modal characteristic parameter through a fault diagnosis model cooperatively optimized by the cloud platform and the substation auxiliary control device, and judges and identifies whether the substation equipment has a fault and the type and severity of the fault. According to the invention, the efficiency and accuracy of transformer substation fault diagnosis are improved.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

The invention discloses an intelligent building multi-dimensional monitoring system supporting edge computing and cross-terminal collaborative management, and relates to the technical field of intelligent buildings, the intelligent building multi-dimensional monitoring system comprises a multi-area edge monitoring node and an asynchronous communication cloud management platform, the edge node is provided with a lightweight dynamic collaborative engine, a node database and equipment management and control module, and a management and control terminal device; data are collected and standardized, simple / complex events are identified, risk levels are divided, and autonomous networking is carried out to form a temporary collaborative cluster when the risk is high; the data interaction module adopts UDP lightweight protocol low-delay transmission and local retransmission to guarantee data integrity, the cloud platform stores event processing abstracts, analyzes historical data optimization strategies and rules and silently issues in idle periods, the visual module assists monitoring and decision making, the system realizes distributed sensing and collaborative management and control, cloud dependence is reduced, and the real-time performance of the system is improved. The event response efficiency and the resource utilization rate are improved.

Techniques for optimized LLM inference from structured data via intermediate documents (“LLMs”) are disclosed. In an example method, a computing device accesses a database including a set of collections. The computing device generates one or more documents based on a first collection of the set of collections. The computing device determines one or more portions of the one or more documents based on at least one topic of the one or more documents. The computing device generates an embedded representation of each portion. The computing device receives, from a client device, a first query, including at least a first topic. The computing device determines a first portion of the one or more portions based on the first topic. The computing device generates a response based on the first query and the first portion and outputs the response to the client device.

The invention provides a K8S-based satellite image recognition resource dynamic elastic scheduling system. The system comprises a task perception module, a resource evaluation module, an elastic scheduling module, a fault tolerance module and a learning optimization module. The task sensing module collects satellite image recognition task metadata and transfers the metadata to a priority label library; the resource evaluation module monitors node resources, quantifies the health degree and screens healthy nodes into a candidate pool; the elastic scheduling module executes Pod dynamic capacity expansion and contraction and other decisions according to tasks and resources; the fault-tolerant module identifies the fault Pod, reconstructs and distributes a new Pod according to an anti-affinity rule; the learning optimization module optimizes the system parameters according to the historical data. The invention also provides a dynamic elastic scheduling method. Therefore, the resource utilization rate and the system stability of the satellite image recognition task are remarkably improved.

The invention discloses a mine pressure monitoring data optimization and feature extraction method, which comprises the following steps of: firstly, establishing a multi-channel and high-frequency original time sequence data set through continuous acquisition of underground multi-type sensors, and providing an input basis for subsequent fluctuation detection; and then, calculating the adjacent ratio of the data sequence point by point, and adaptively determining a threshold value in combination with a sliding window quantile statistical result, thereby realizing dynamic identification of high and low fluctuation points and providing a marking basis for an optimization strategy. And then, automatically matching a corresponding optimization strategy according to the distribution form of the fluctuation points, performing targeted smoothing and recovery on the abnormal data by means of point pair correlation correction, isolated point backtracking correction, continuous section batch correction, bottom backfilling and the like, and outputting an optimization sequence. And finally, taking the corrected data as new input, performing loop iterative calculation, and continuously optimizing parameters and strategies through correlation, smoothness and information entropy evaluation until indexes converge to obtain a final mine pressure monitoring data result with trend fidelity and noise suppression.

The invention discloses a raw coal quality data optimization and reconstruction method based on clean coal ash content feedback, and belongs to the technical field of computers. Data completion is achieved through the generative adversarial network; a generator predicts missing raw coal data according to the high-sampling-frequency clean coal ash content, and a discriminator evaluates the authenticity of the data. According to the method, the characteristic that the clean coal ash sampling rate is higher in the dense medium separation process is used for guiding data reconstruction; meanwhile, a composite loss function including adversarial loss, reconstruction loss and regression loss is designed, and the generation quality is improved. The method effectively solves the problem of raw coal quality data missing in the coal dressing process, and has high engineering application value.

The invention discloses a multispectral image and LiDAR fusion method suitable for dam body deformation monitoring, and belongs to the field of hydraulic engineering safety monitoring. According to the method, a dam body surface multispectral image and three-dimensional point cloud data are obtained through an unmanned aerial vehicle, and crack spectral features and structural joint geometric features are extracted after processing; constructing a CNN and finite element coupling model, inputting the surface displacement field after feature fusion into the finite element model, and simulating internal stress-strain distribution in combination with material parameters; and analyzing the space-time correlation between the surface displacement and the internal stress, optimizing parameters by combining measured data, and outputting a deformation correlation result. According to the invention, all-directional correlation monitoring of the surface and internal deformation of the dam body is realized, the limitation of a single data source is overcome, the monitoring precision and reliability are improved, and support is provided for safety assessment of the dam body.

The invention provides a data optimization method and system based on self-adaptive multi-stage compression, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-compressed data; preprocessing the to-be-compressed data to obtain preprocessed data; performing primary compression on the preprocessed data to obtain primary compressed data; according to the access frequency and the storage requirement of the first-level compressed data, judging whether the first-level compressed data needs to be subjected to second-level compression or not; if yes, entering the next step; otherwise, storing the first-level compressed data into a rapid storage medium; and carrying out secondary compression on the primary compressed data by adopting a progressive storage partitioning technology to obtain secondary compressed data, and storing the secondary compressed data into a low-speed storage medium. The adaptive multi-stage compression technology is adopted to ensure that a lightweight compression strategy is used for frequently accessed data, so that additional processing overhead for high-frequency accessed data is avoided, and deeper two-stage compression is performed on low-frequency accessed data to save storage space to the greatest extent.

The invention discloses a customer service telephone traffic platform real-time scheduling data optimization method based on edge computing, and relates to the technical field of communication. The customer service telephone traffic platform real-time scheduling data optimization method based on edge computing comprises the following steps: S1, collecting resource data and state monitoring data in an edge computing environment for preprocessing, and constructing a telephone traffic scheduling database after storing the resource data and the state monitoring data; s2, carrying out stability analysis through resource load, response time and preheating progress; s3, fusing task execution, data synchronization, business adaptation and node stability, and evaluating the preheating state of the cold seat; s4, generating a traffic adaptation index through the resource load, the preheating state and the task queue data; and S5, monitoring the resource load, the task queue and the response time of the node in real time to form a load feedback signal, optimizing a task scheduling strategy and generating a load report. The problems that resource scheduling of a customer service telephone traffic platform is insufficient and the preheating state of a cold seat is indefinite under the conditions of high load and high concurrency are solved.

The invention discloses an intelligent mechanical fault rapid diagnosis method. The method comprises the steps of multi-source data acquisition, data optimization processing, mechanical abnormity rapid detection, mechanical fault rapid identification, model performance optimization and mechanical fault intelligent diagnosis. The invention relates to the technical field of mechanical fault data processing, in particular to an intelligent mechanical fault rapid diagnosis method, which innovatively proposes a mechanical fault rapid diagnosis technology for abnormal trigger key sequence extraction, and improves the sensitivity and discrimination precision of mechanical fault features. A random convolution disturbance mechanism is introduced, and a multi-equipment adaptability enhanced loss function is designed, so that rapid, accurate and intelligent diagnosis of mechanical faults under complex working conditions and multi-equipment conditions is realized; an optimization algorithm is improved by adopting a disturbance guide extension strategy, a jump drive fine tuning mechanism and a multi-channel fine tuning candidate strategy, a model performance optimal parameter combination is obtained, and the stability and recognition precision of a mechanical fault recognition model are enhanced.